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New York, New York, United States
Onsite
Gynger is looking for a sharp, analytically minded Financial & Credit Analytics Analyst to help build the data, modeling, and reporting foundation behind our lending products. This is a high impact opportunity to shape how a fast growing fintech uses data to make smarter credit decisions, monitor portfolio performance, and scale loan operations.
You'll work at the intersection of credit risk, finance, and engineering - turning raw data into the models, dashboards, and insights that drive underwriting, servicing, and investor reporting. The ideal candidate is equal parts quantitative analyst and problem solver: comfortable in SQL and Excel, fluent in financial concepts, and excited by the prospect of helping define a new function within the company.
Based in our NYC HQ, reporting to the SVP of Finance and working closely with our Chief Credit Officer.
Credit analytics & modeling: Build and maintain analyses and models that support underwriting, credit approvals and re-approvals, risk segmentation, and portfolio monitoring.
Data & reporting infrastructure: Own the queries, datasets, and dashboards that power credit and finance reporting, ensuring data is accurate, timely, and trusted across the company.
KPI tracking: Monitor key credit, lending, and business KPIs and surface insights for Executives, Sales, and the Credit team.
Debt facility & investor reporting: Support reporting and compliance for our credit facilities, prepare materials for investor data rooms, and respond to diligence requests with clean, well-structured analyses.
Loan servicing analytics: Partner with operations and engineering on the data and logic behind loan modifications, collections, billing, and other servicing workflows.
2 - 5 years of experience in a quantitative, analytical, or finance role ideally in credit, lending, fintech, or investment banking.
Direct experience in the credit or lending space (origination, underwriting, servicing, or portfolio management).
Strong SQL skills and advanced Excel proficiency, working knowledge of Python (or similar) is a plus.
Solid grounding in financial and credit concepts - credit risk, cash flow, and basic GAAP accounting.
Experience with BI tools (Looker, Tableau, Metabase, etc.) and comfort working in modern data environments.
Bachelor's degree in Finance, Economics, Mathematics, Statistics, Computer Science, or a related quantitative field.
Exceptional attention to detail and a builder's mindset - curious, entrepreneurial, and comfortable operating with ambiguity in a fast-moving startup.
